SF.net SVN: matplotlib: [3979] trunk/py4science/examples/skel

[<jd...@us...>]

Revision: 3979
          http://matplotlib.svn.sourceforge.net/matplotlib/?rev=3979&view=rev
Author:   jdh2358
Date:     2007-10-21 14:08:53 -0700 (Sun, 21 Oct 2007)

Log Message:
-----------
rename skel

Added Paths:
-----------
    trunk/py4science/examples/skel/glass_dots1_skel.py

Removed Paths:
-------------
    trunk/py4science/examples/skel/glass_dots_skel.py

Copied: trunk/py4science/examples/skel/glass_dots1_skel.py (from rev 3978, trunk/py4science/examples/skel/glass_dots_skel.py)
===================================================================
--- trunk/py4science/examples/skel/glass_dots1_skel.py	                        (rev 0)
+++ trunk/py4science/examples/skel/glass_dots1_skel.py	2007-10-21 21:08:53 UTC (rev 3979)
@@ -0,0 +1,77 @@
+"""
+Moire patterns from random dot fields
+
+http://en.wikipedia.org/wiki/Moir%C3%A9_pattern
+
+See L. Glass. 'Moire effect from random dots' Nature 223, 578580 (1969).
+"""
+from numpy import cos, sin, pi, matrix
+import numpy as npy
+import numpy.linalg as linalg
+from pylab import figure, show
+
+def csqrt(x):
+    """
+    sqrt func that handles returns sqrt(x)j for x<0
+    """
+    XXX
+    
+def myeig(M):
+    """
+    compute eigen values and eigenvectors analytically
+
+    Solve quadratic:
+
+      lamba^2 - tau*lambda + Delta = 0
+
+    where tau = trace(M) and Delta = Determinant(M)
+    
+    """
+    XXX
+    return lambda1, lambda2
+    
+# 2000 random x,y points in the interval[-0.5 ... 0.5]
+X1 = XXX
+
+name =  'saddle'
+#sx, sy, angle = XXX
+
+#name = 'center'
+#sx, sy, angle = XXX
+
+#name = 'stable focus'  # spiral
+#sx, sy, angle = XXX
+
+name= 'spiral'
+sx, sy, angle = XXX
+
+theta = angle * pi/180.  # the rotation in radians
+
+
+# the scaling matrix
+# | sx 0 |
+# | 0 sy |
+S = XXX
+
+# the rotation matrix
+# | cos(theta)  -sin(theta) |
+# | sin(theta)  cos(theta)  |
+R = XXX
+
+# the transformation is the matrix product of the scaling and rotation
+M = XXX
+
+# compute the eigenvalues using numpy linear algebra
+vals, vecs = XXX
+print 'numpy eigenvalues', vals
+
+# compare with the analytic values from myeig
+avals = myeig(M)
+print 'analytic eigenvalues', avals
+
+# transform X1 by the matrix M
+X2 = XXX
+
+# plot the original x,y as green dots and the transformed x, y as red
+# dots
+show()

Deleted: trunk/py4science/examples/skel/glass_dots_skel.py
===================================================================
--- trunk/py4science/examples/skel/glass_dots_skel.py	2007-10-21 21:08:33 UTC (rev 3978)
+++ trunk/py4science/examples/skel/glass_dots_skel.py	2007-10-21 21:08:53 UTC (rev 3979)
@@ -1,77 +0,0 @@
-"""
-Moire patterns from random dot fields
-
-http://en.wikipedia.org/wiki/Moir%C3%A9_pattern
-
-See L. Glass. 'Moire effect from random dots' Nature 223, 578580 (1969).
-"""
-from numpy import cos, sin, pi, matrix
-import numpy as npy
-import numpy.linalg as linalg
-from pylab import figure, show
-
-def csqrt(x):
-    """
-    sqrt func that handles returns sqrt(x)j for x<0
-    """
-    XXX
-    
-def myeig(M):
-    """
-    compute eigen values and eigenvectors analytically
-
-    Solve quadratic:
-
-      lamba^2 - tau*lambda + Delta = 0
-
-    where tau = trace(M) and Delta = Determinant(M)
-    
-    """
-    XXX
-    return lambda1, lambda2
-    
-# 2000 random x,y points in the interval[-0.5 ... 0.5]
-X1 = XXX
-
-name =  'saddle'
-#sx, sy, angle = XXX
-
-#name = 'center'
-#sx, sy, angle = XXX
-
-#name = 'stable focus'  # spiral
-#sx, sy, angle = XXX
-
-name= 'spiral'
-sx, sy, angle = XXX
-
-theta = angle * pi/180.  # the rotation in radians
-
-
-# the scaling matrix
-# | sx 0 |
-# | 0 sy |
-S = XXX
-
-# the rotation matrix
-# | cos(theta)  -sin(theta) |
-# | sin(theta)  cos(theta)  |
-R = XXX
-
-# the transformation is the matrix product of the scaling and rotation
-M = XXX
-
-# compute the eigenvalues using numpy linear algebra
-vals, vecs = XXX
-print 'numpy eigenvalues', vals
-
-# compare with the analytic values from myeig
-avals = myeig(M)
-print 'analytic eigenvalues', avals
-
-# transform X1 by the matrix M
-X2 = XXX
-
-# plot the original x,y as green dots and the transformed x, y as red
-# dots
-show()


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